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Data Collection and Quality Control

  • Chapter
Fundamentals of Clinical Trials

Abstract

Valid and informative results from clinical trials depend on data that are of high enough quality and sufficiently robust to address the question posed. Such data in clinical trials are collected from several sources—medical records (electronic and paper), interviews, questionnaires, participant examinations, laboratory determinations, or public sources like national death registries. Data elements vary in their importance, but having valid data regarding key descriptors of the population, the intervention, and primary outcome measures is essential to the success of a trial. Equally important, and sometimes a trade-off given limited resources, is having a large enough sample size and number of outcome events to obtain a sufficiently narrow estimate of the intervention effect. Modest amounts of random errors in data will not usually affect the interpretability of the results, as long as there are sufficient numbers of outcome events. However, systematic errors can invalidate a trial’s results.

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Author information

Authors and Affiliations

  1. North Bethesda, MD, USA

    Lawrence M. Friedman

  2. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Curt D. Furberg

  3. Department Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA

    David L. DeMets

  4. Department of Biostatistics, Wake Forest School of Medicine, Winston-Salem, NC, USA

    David M. Reboussin

  5. Department of Medicine, Duke University, Durham, NC, USA

    Christopher B. Granger

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  1. Lawrence M. Friedman
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  2. Curt D. Furberg
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  4. David M. Reboussin
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  5. Christopher B. Granger
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Friedman, L.M., Furberg, C.D., DeMets, D.L., Reboussin, D.M., Granger, C.B. (2015). Data Collection and Quality Control. In: Fundamentals of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-18539-2_11

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